Signal Processing Roadmap

Technologies, Applications, and Future Directions

Pushan Kumar Dutta, Pethuru Raj, Pronaya Bhattacharya, Ishan Budhiraja, Dmitrii Kaplun

• Publisher: Morgan Kaufmann
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9780443331732
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields. The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets. - Provides a comprehensive reference for signal processing techniques in modern measurement systems - Highlights the latest innovations and future directions that drive transformative capabilities - Offers a roadmap for signal processing advances across application domains like 6G networks, pervasive health monitoring, and industry 4.0 - Discusses emerging trends in areas like photonic signal processing, virtual/augmented reality, additive manufacturing, and autonomous robots - Brings critical analysis of signal processing and uncertainty modeling for enabling next-generation smart measurement systems

Table of contents

• Title of Book
• Chapter 1 Foundations of signal processing: a comprehensive overview
• Chapter 2 Frontiers in digital and analog signal processing circuits and systems in the era of artificial intelligence (AI)
• Chapter 3 Sustainable 6G connectivity through artificial intelligence powered energy efficiency
• Chapter 4 Computing requirements and techniques to support signal processing
• Chapter 5 Signal processing techniques for 6G wireless networks: potential applications, trends, and challenges
• Chapter 6 Signal processing for localization and sensing techniques in next-generation wireless networks
• Chapter 7 Advanced signal processing techniques for nonorthogonal multiple access in 6G networks
• Chapter 8 Signal processing innovations for improved 6 G ubiquitous connectivity: paving the path towards integrated space-ground-underwater networks
• Chapter 9 Advanced real-time signal processing techniques for adaptive beamforming in beyond 5G wireless networkss
• Chapter 10 Signal processing for autonomous systems and robotics: principles, techniques, and future directions
• Chapter 11 Signal processing: a roadmap towards image reconstruction and image enhancement
• Chapter 12 Study of image enhancement techniques for satellite images
• Chapter 13 Intelligent signal processing for physical and medium access control layers: a machine learning perspective
• Chapter 14 Optimization techniques in 6 G communication: from foundations to AI-driven solutions
• Chapter 15 Advanced image restoration via lightweight convolutional networks and graph-based contextual learning
• Chapter 16 Emerging trends in edge-AIoriented signal processing for IoT devices
• Chapter 17 Quantum-enhanced fuzzy convolutional neural networks for high-performance analog signal processing systems
• Chapter 18 Resource optimization, error mitigation, and algorithmic partitioning in multinode quantum systems: a practical perspective
• Chapter 19 Artificial intelligence-driven heterogeneous wireless network optimization: composite fading, mobility modeling, and resource allocation
• Chapter 20 Intelligent traffic management system with machine learning for congestion prediction and control
• Index